Does anyone know how to calculate elevation using lattitude and longitude. By calculation I mean formula to calculate it manually. Other than using lattitude and longitude is there any other way to find elevation from any other parameters ?
EDIT 1
To be more specific regarding my above question other than using Elevation API provided by Google is there any way we could calculate elevation using any data from GPS of device.
Also Is it possible to calculate elevation from altitude..?
No, it is not possible to derive elevation from altitude or longitude or latitude or any combination of those things.
There are different ways to look at this problem, and I wouldn't say it is impossible.
If the surface elevation model is represented by an equation, such as with spherical harmonics, you could compute the elevation from this model, given a tuple of latitude and longitude.
You could also precompute the elevations on an appropriately sized grid and look up the values as is done in this answer.
Related
I have given two DMEs and their slant ranges to aircraft which can be converted to angular distances. The altitude information is also provided from which we can find angles between stations and the center of earth. how to implement this on Matlab?
I have a large set of data with 7 stations along a river. During the collection of the data, samples were taken up and down a range of the river to get a representative idea of the environment. These aren't insignificant distances, the reaches can be over 50 miles over a curvy river. However, for the purposes of analysis, I've been asked to compress this data to a representative site in the middle of this reach using the latitude and longitude. This would be easy to do in GIS, but working with R I haven't been able to find a good method. I've tried taking the median of both the latitude and longitude and comparing them, but there's no overlap and I don't think that's a justifiable method. Is there a good package in R or a mathematical trick that I'm missing to find a median latitude, longitude pair?
I have a list of locations with zipcodes. I have another list of Distribution Centers that serve these locations. Is there anyway to map the nearest DC to each of these locations? I am an extremely green coder, but i have some experience with R
I'd need more information to give you some possible code to solve your problem however, here is one approach to solving your problem.
Convert your zipcodes to longitudes and latitudes.
Not sure what location data you have on your distribution centers, but you should be able to find a way to retrieve the long/lat of each of these.
For each zipcode, compute the the distance to each DC (using their respective longs/lats). To compute the distance, use the haversine formula. Find the minimum of these distances. This is your solution.
I work on a project building recognition system. I want to ask the experienced people in collecting GPS data from a map.
I want to partition the map into grids the area of each is 30 meter * 30 meter in each grid i want to store the center GPS coordinate (i.e point(15, 15)
What is the best way to do this?
Here's an image that demonstrates what I need.
This is not so easy:
There are two ways:
The professional solution:
Draw the grid 30x30 using the UTM coordinate system of that country / city.
UTM is measured in meters and is a flat cartesian coordinate system, while latitude / longitude are spehrical and not linear in x,y.
Align your grid such that it corresponds to integral UTM coordinates.
Then you need a method sto transform from latitude/longitude WGS84 to UTM (hopefully WGS84, but in some countries other ellipsoids are used)
The map display software should be apple to use UTM coordinate system.
And the simpler one:
stay using the lat long cooridnates and calculate the latitudinalGridWithDegrees measure in degrees wich corresponds to 30m (in the middle of the map/ city/ curch of city)
Since lat and lon do not use the same scale (1 degree of latitude differecne is not the same meters as one degree in longitude - only at the aequator), additionally calculate the longitudinalGridWithDegrees.
You will get two different values (they differ by a factor of cos(mapCenterLatitudeRadians).
To calculate this values either understand geo calculations or simply use a function which creates an offset point by given radius and direction from an start point. Use the center of the map as point to be offset (start point).
Create one point with offset 30m ,and heading = 0°, then measure the lat difference by subtracting and store in latitudinalGridWithDegrees
Do the same using heading = 90, and measure the longitudinal difference and store in longitudinalGridWithDegrees.
Now you are able two draw the grid using as lat and long steps the value of that both variables.
These then gives the corner points of such an square, and you always use latitude and longituide as interfcae to the mapping software.
If you are living in special countries/continents like Australia or Norwegen where the continent drift with up to 1m per year, it is more difficult.
Other continents drift only 1mm per year, which you and all other apps just ignore.
Advantages / Disadvantages of each solution
Simple Solution:
- grid is not square at corners of map, but probabyl not visisble for a city
- grid cell is exactly 30m only in the center of the map / reference point choosen for offset calc)
+ easier implementtaion
+ maping software interfcae simpler and always supported
Professional solution.
+ grid will match professional paper maps
+ grid cell is always exactly 30m
- needs geo transformation software or method
- unclear whether map display software provdes UTM (in most cases not)
However, maybe it is easier to assign road and house numbers to be measured, supported to match more ore less the grid.
Mine are follow-ups to the question & answer in Approaches for spatial geodesic latitude longitude clustering in R with geodesic or great circle distances.
I would like to better understand:
Question #1: If all the lat / long values are within the same city, is it necessary to use either fossil or distHaversine(...) to first calculate great circle distances ?
or, within a single city, is it OK to run clustering on the lat/long values themselves ?
Question #2: jlhoward suggests that :
It's worth noting that these methods require that all points must go into some cluster. If you just ask which points are close together, and allow that some cities don't go into any cluster, you get very different results.
In my case I would like to ask just ask "which points are close together", without forcing every point into a cluster. How can I do this ?
Question #3: To include one or two factor variables into the clustering (in addition to lat/long), is it as easy as including those factor variables in the df upon which the clustering is run ?
Please confirm.
Thanks!
"within a single city, is it OK to run clustering on the lat/long values themselves ?"
Yes, as long as your city is on the equator, where a degree of longitude is the same distance as a degree of latitude.
I'm standing very close to the north pole. One degree of longitude is 1/360 of the circumference of the circle round the pole from me. Someone ten degrees east of me might only be ten feet away. Someone one degree south of me is miles away. A clustering algorithm based on lat-long would think that guy miles away was closer to me than the guy I can wave to ten degrees east of me.
The solution for small areas to save having to compute great-circle ellipsoid distances is to project to a coordinate system that is near-enough cartesian so that you can use pythagoras' theorem for distance without too much error. Typically you would use a UTM zone transform, which is essentially a coordinate system that puts its equator through your study area.
The spTransform function in sp and rgdal will sort this out for you.